Decagon Engineering Manager, Research at Decagon leads a team developing conversational AI models and decision-making stacks. The role involves managing technical roadmaps, driving research to production impact, and developing engineers.
Responsibilities
We’re looking for a manager to lead and grow a team within Decagon Research. You’ll own the team and technical roadmap for an area spanning model post-training, evaluation, safety, speech, or agent behavior, driving ambitious ideas from research to reliable production impact.
Managers here operate with high ownership and technical depth while helping their teams move quickly and maintain a high quality bar. You’ll create clarity in ambiguous research areas, develop exceptional engineers, and align the team’s work with the product outcomes that matter most to customers.
Build, manage, and develop a high-performing team of research engineers through hiring, coaching, performance management, and career development
Own the technical direction and roadmap for your area, prioritizing the highest-leverage investments across models, evaluation, safety, speech, and agent systems
Turn open-ended research directions into focused programs with clear hypotheses, milestones, decisions, and measures of production impact
Stay close to the technical work through architecture and experiment reviews, debugging, and individual contribution
Set a high bar for experimental rigor, reproducibility, evaluation quality, engineering execution, and safe model rollouts
Partner across Infrastructure, Agent Platform, Product Engineering, Product, and customer-facing teams to move research into production
Qualification
Deep experience post-trainingExperience hiringEven better if you haveA record of influential research
Required
5+ years of industry experience building machine-learning systems involving language models, speech, agents, or model infrastructure
1+ years of people management experience, with a strong individual-contributor background
Deep experience post-training, evaluating, deploying, or serving foundation models at scale
Strong technical judgment and a track record of translating ambiguous research ideas into measurable product outcomes
Experience hiring, coaching, and developing technical talent, with clear communication across research, engineering, product, and executive stakeholders
Even better if you have
A record of influential research, open-source contributions, or production ML systems with significant real-world impact
